Recurrent mixture density networksSparse neural networksHighly variable time seriesForecastingConfidence estimation of predictionDimensionality reductionAccurate forecasting of a high variability time series has relevance in many applications such as supply-chain management, price prediction in stock markets and ...
[28] M. Schuster. Better generative models for sequential data problems: Bidirectional recurrent mixture density networks. pages 589–595. The MIT Press, 1999. [29] I. Sutskever, G. E. Hinton, and G. W. Taylor. The recurrent temporal restricted boltzmann machine. pages 1601–1608, 2008. [...
[1] Y. Bengio, P. Simard, and P. Frasconi. Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2):157–166, March 1994. [2] C. Bishop. Mixture density networks. Technical report, 1994. [3] C. Bishop. Neural Networks for Pattern ...
Generating Sequences With Recurrent Neural Networks
A recurrent spiking neural network is proposed that implements planning as probabilistic inference for finite and infinite horizon tasks. The architecture splits this problem into two parts: The stochastic transient firing of the network embodies the dynamics of the planning task. With appropriate inject...
Fast detection of slender bodies in high density microscopy data Article Open access 19 July 2023 Enhancing yeast cell tracking with a time-symmetric deep learning approach Article Open access 13 March 2025 Deep neural networks enable quantitative movement analysis using single-camera videos Arti...
(c) Three types of recurrent neural networks (RNNs) were used to learn the microstructure evolution in latent space. These include simple RNN, GRU, and LSTM. (d) and (e) The trained RNN models can be used to predict the microstructure in latent space for future sequences, and ...
Note the ‘skip connections’ from the inputs to all hidden layers, and from all hidden layers to the outputs. These make it easier to train deep networks, by reducing the number of processing steps between the bottom of the network and the top, and thereby mitigating the ‘vanishing gradie...
Theoretical background; Methods used; Findings and discussion. Watanabe,Sumio - 《IEEE Transactions on Neural Networks》 被引量: 21发表: 2001年 加载更多研究点推荐 Sequential Bayesian Estimation Recurrent Neural Networks Gaussian mixture approximation sequential or recursive Bayesian estimation 0...
Brain networks exist within the confines of resource limitations. As a result, a brain network must overcome the metabolic costs of growing and sustaining the network within its physical space, while simultaneously implementing its required information p